Short term solar irradiance forecasting using sky images based on a hybrid CNN–MLP model

High penetration of photovoltaics (PV) has been observed in the energy market over the last decade. However, its integration into electrical grids is challenging, as solar energy is highly fluctuating given its dependence on different weather variables. Consequently, short-term forecasting of solar...

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Autores principales:	Omaima El Alani, Mounir Abraim, Hicham Ghennioui, Abdellatif Ghennioui, Ilyass Ikenbi, Fatima-Ezzahra Dahr
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Solar irradiance Short term forecasting Sky images Artificial intelligence Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/243bc3a496a34a55b6169f8975b869e3
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id	oai:doaj.org-article:243bc3a496a34a55b6169f8975b869e3
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spelling	oai:doaj.org-article:243bc3a496a34a55b6169f8975b869e32021-11-18T04:49:23ZShort term solar irradiance forecasting using sky images based on a hybrid CNN–MLP model2352-484710.1016/j.egyr.2021.07.053https://doaj.org/article/243bc3a496a34a55b6169f8975b869e32021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352484721005199https://doaj.org/toc/2352-4847High penetration of photovoltaics (PV) has been observed in the energy market over the last decade. However, its integration into electrical grids is challenging, as solar energy is highly fluctuating given its dependence on different weather variables. Consequently, short-term forecasting of solar irradiance provides a pivotal solution to ensure optimal use of the produced energy and reduce its uncertainty. This study proposes a hybrid convolutional neural network and Multilayer perceptron (CNN–MLP) model to forecast the global irradiance 15 min ahead. The model uses images from a hemispherical sky imager, time series of GHI, and weather variables collected from a ground meteorological station in Morocco. The evaluation of the proposed model under clear, mixed, and overcast days shows that the proposed model performs better than the persistence model. The root mean square error (RMSE) varies between 13.05 W/m2 and 49.16 W/m2 for CNN–MLP and between 45.76 W/m2 and 114.19 W/m2 for persistence. The coefficient of determination (R2) varies between 0.99 and 0.94 for the MLP–CNN and between 0.98 and 0.79 for persistence. The results show that the proposed model could be an appropriate choice for short-term forecasting even under cloudy conditions.Omaima El AlaniMounir AbraimHicham GhenniouiAbdellatif GhenniouiIlyass IkenbiFatima-Ezzahra DahrElsevierarticleSolar irradianceShort term forecastingSky imagesArtificial intelligenceElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENEnergy Reports, Vol 7, Iss , Pp 888-900 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Solar irradiance Short term forecasting Sky images Artificial intelligence Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Solar irradiance Short term forecasting Sky images Artificial intelligence Electrical engineering. Electronics. Nuclear engineering TK1-9971 Omaima El Alani Mounir Abraim Hicham Ghennioui Abdellatif Ghennioui Ilyass Ikenbi Fatima-Ezzahra Dahr Short term solar irradiance forecasting using sky images based on a hybrid CNN–MLP model
description	High penetration of photovoltaics (PV) has been observed in the energy market over the last decade. However, its integration into electrical grids is challenging, as solar energy is highly fluctuating given its dependence on different weather variables. Consequently, short-term forecasting of solar irradiance provides a pivotal solution to ensure optimal use of the produced energy and reduce its uncertainty. This study proposes a hybrid convolutional neural network and Multilayer perceptron (CNN–MLP) model to forecast the global irradiance 15 min ahead. The model uses images from a hemispherical sky imager, time series of GHI, and weather variables collected from a ground meteorological station in Morocco. The evaluation of the proposed model under clear, mixed, and overcast days shows that the proposed model performs better than the persistence model. The root mean square error (RMSE) varies between 13.05 W/m2 and 49.16 W/m2 for CNN–MLP and between 45.76 W/m2 and 114.19 W/m2 for persistence. The coefficient of determination (R2) varies between 0.99 and 0.94 for the MLP–CNN and between 0.98 and 0.79 for persistence. The results show that the proposed model could be an appropriate choice for short-term forecasting even under cloudy conditions.
format	article
author	Omaima El Alani Mounir Abraim Hicham Ghennioui Abdellatif Ghennioui Ilyass Ikenbi Fatima-Ezzahra Dahr
author_facet	Omaima El Alani Mounir Abraim Hicham Ghennioui Abdellatif Ghennioui Ilyass Ikenbi Fatima-Ezzahra Dahr
author_sort	Omaima El Alani
title	Short term solar irradiance forecasting using sky images based on a hybrid CNN–MLP model
title_short	Short term solar irradiance forecasting using sky images based on a hybrid CNN–MLP model
title_full	Short term solar irradiance forecasting using sky images based on a hybrid CNN–MLP model
title_fullStr	Short term solar irradiance forecasting using sky images based on a hybrid CNN–MLP model
title_full_unstemmed	Short term solar irradiance forecasting using sky images based on a hybrid CNN–MLP model
title_sort	short term solar irradiance forecasting using sky images based on a hybrid cnn–mlp model
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/243bc3a496a34a55b6169f8975b869e3
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Short term solar irradiance forecasting using sky images based on a hybrid CNN–MLP model

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